To analyze the computational complexity of our IM-META method, it is not difficult to show that the complexity for the network discovery phase is dominated by the network inference step, whose complexity is given by \(\mathcal {O}(n^2)\) , where \(n\)  is the number of nodes in \(\mathcal {V}\) , conditioned that the size of model parameters in MLPs does not scale with \(n\) . Of course, the computational complexity for this phase can be greatly reduced when parallelization is applied. Thus, the bottleneck of IM-META is the greedy IM algorithm in the seed set selection phase, whose complexity is bounded by \(\mathcal {O}(Rknm)\)  (refer to [1]} for more details), where \(R\)  represents the number of Monte-Carlo simulations to estimate the expected influence spread and \(m\)  is the number of edges in \(\mathcal {E}\) . As \(R\)  and \(k\)  are regarded as constants and \(n < m\) , the total complexity of IM-META is finally given by \(\mathcal {O}(nm)\) , which lies in the same line as that of the greedy IM algorithm on the underlying graph \(G\) .
